Editorial: Regulation of Cellular Reprogramming for Post-stroke Tissue Regeneration: Bridging a Gap Between Basic Research and Clinical Application
Bibliographic record
Abstract
Bridging a Gap Between Basic Research and Clinical ApplicationStroke is a leading cause of death and disability world-wide.Stroke patients often live with longterm motor and cognitive impairments.There is currently no effective treatment to reverse or significantly improve these neurological outcomes.The development of cellular reprogramming technology has the potential to provide novel cell-based strategies to treat stroke-related brain injury and dysfunction.Building on well described tenets of developmental biology, the landmark discovery of induced pluripotent stem cells using defined transcription factors has since been advanced to allow the direct reprogramming of one somatic cell type to another to generate cells lost to injury or disease.This research topic explores avenues to bridge the gap between basic research and clinical application as it relates to identifying strategies that utilize cellular reprogramming for post-stroke tissue repair.The topic builds on recent advances in understanding regulatory mechanisms and approaches that can successfully reprogram nonneuronal cells into neurons through a pluripotent intermediate or bypassing pluripotency to enable neural repair.We appreciate all the researchers who participated in this topic, in which five papers were published (two original research papers and three review papers).The research presented provides valuable information and insights on cellular reprogramming as a novel therapeutic option for post-stroke tissue regeneration and functional recovery, including consideration of the microenvironment and it's impact on reprogrammed cells.A short description of these papers follows.In the original paper authored by Ge et al., researchers used Rhesus Macaque monkeys, nonhuman primates, to demonstrate that overexpression of a single neural transcription factor NeuroD1 in reactive astrocytes following ischemic injury can convert them into neurons at the injury site.Following the in vivo astrocyte-to-neuron (AtN) conversion, the neuronal density and synaptic markers in the NeuroD1-treated injury areas were significantly increased, accompanied with increased survival of parvalbumin interneurons and reduced number of microglia and
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".